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Table of Contents
Intro; Preface; Contents; Abbreviations; List of Figures; List of Tables; 1 Introduction; 1.1 Background; 1.2 Motivation; 1.3 Overview; References; 2 Review of Related Work; 2.1 Accelerometer Applications; 2.1.1 Small-Sized Rover Platform Using Accelerometer; 2.1.2 Road Vehicle Using Accelerometer; 2.2 Camera Applications; 2.2.1 Small-Sized Rover Using Camera; 2.2.2 Road Vehicle Using Camera; 2.3 LRF Applications; 2.3.1 Small-Sized Rover Using LRF; 2.3.2 Road Vehicle Using LRF; 2.4 Mutiple-sensor Applications; 2.4.1 Small-Sized Rover Using Multiple Sensors
2.4.2 Road Vehicle Using Multiple Sensors2.5 Conclusion; References; 3 Acceleration-Based Road Terrain Classification; 3.1 Road Profile Estimation; 3.1.1 Acceleration (acc-t); 3.1.2 Quarter Vehicle Model (acc-t to y-t); 3.1.3 Vertical Displacement (y-t); 3.1.4 Speed (v-t); 3.1.5 Speed to Displacement (v-t to x-t); 3.1.6 Road Profile (y-x); 3.2 Features Extraction; 3.2.1 FFT Feature Extracted from Road Profile (y-x); 3.2.2 FFT Feature Extracted from Acceleration (acc-t); 3.2.3 Fast Wavelet Transform Feature Extracted from Acceleration (acc-t) and Road Profile (y-x); 3.3 Normalization
3.4 Principal Component Analysis3.5 K-Fold Cross-Validation; 3.6 Alternative Classifiers; 3.6.1 Naïve Bayes Classifier; 3.6.2 Neural Network Classifier; 3.6.3 Support Vector Machines Classifier; 3.7 Experiment; 3.7.1 Experiment Platform; 3.7.2 Acceleration-Based Experiments; 3.8 Experiment Results; 3.8.1 Feature Selection; 3.8.2 Speed Dependency; 3.8.3 Classifiers Selection; 3.8.4 Acceleration-Based Experiment Result; 3.9 Conclusion; References; 4 Image-Based Road Terrain Classification; 4.1 Texture Features from Image; 4.2 Image Feature Matrix Establishment
4.2.1 Grey-Level Co-occurrence Matrix4.2.2 Feature Extraction and Feature Matrix Formation; 4.3 Experiment; 4.3.1 Experimental Platform; 4.3.2 Image-Based Experiments; 4.4 Experiment Results; 4.5 Conclusion; References; 5 LRF-Based Road Terrain Classification; 5.1 Geometric Arrangement of the LRF; 5.2 Reconstruction of the Road Surface; 5.2.1 Range Data Processing; 5.2.2 Speed Data Processing; 5.2.3 Road Surface; 5.3 Feature Matrix; 5.4 Experiment; 5.4.1 Experimental Platform; 5.4.2 LRF-Based Experiments; 5.5 Experiment Results; 5.5.1 Speed Independency; 5.5.2 LRF-Based Experiment
5.6 ConclusionReferences; 6 Multiple-Sensor Based Road Terrain Classification; 6.1 Predicting LRF-Based Probe; 6.2 Markov Random Field; 6.2.1 Conditional Independence Properties; 6.2.2 Factorization Properties; 6.3 Establishment of MRF Application; 6.3.1 Nodes in MRF; 6.3.2 Variable Values of Nodes in MRF; 6.3.3 Clique Potentials in MRF; 6.3.4 Values of Clique Potentials in MRF; 6.3.5 Energy Function; 6.3.6 Optimization; 6.4 Experiment; 6.4.1 Experimental Platform; 6.4.2 Multiple-Sensor Fusion Based Experiment; 6.5 Experiment Results; 6.6 Conclusion; References; 7 Summary; 7.1 Conclusion
2.4.2 Road Vehicle Using Multiple Sensors2.5 Conclusion; References; 3 Acceleration-Based Road Terrain Classification; 3.1 Road Profile Estimation; 3.1.1 Acceleration (acc-t); 3.1.2 Quarter Vehicle Model (acc-t to y-t); 3.1.3 Vertical Displacement (y-t); 3.1.4 Speed (v-t); 3.1.5 Speed to Displacement (v-t to x-t); 3.1.6 Road Profile (y-x); 3.2 Features Extraction; 3.2.1 FFT Feature Extracted from Road Profile (y-x); 3.2.2 FFT Feature Extracted from Acceleration (acc-t); 3.2.3 Fast Wavelet Transform Feature Extracted from Acceleration (acc-t) and Road Profile (y-x); 3.3 Normalization
3.4 Principal Component Analysis3.5 K-Fold Cross-Validation; 3.6 Alternative Classifiers; 3.6.1 Naïve Bayes Classifier; 3.6.2 Neural Network Classifier; 3.6.3 Support Vector Machines Classifier; 3.7 Experiment; 3.7.1 Experiment Platform; 3.7.2 Acceleration-Based Experiments; 3.8 Experiment Results; 3.8.1 Feature Selection; 3.8.2 Speed Dependency; 3.8.3 Classifiers Selection; 3.8.4 Acceleration-Based Experiment Result; 3.9 Conclusion; References; 4 Image-Based Road Terrain Classification; 4.1 Texture Features from Image; 4.2 Image Feature Matrix Establishment
4.2.1 Grey-Level Co-occurrence Matrix4.2.2 Feature Extraction and Feature Matrix Formation; 4.3 Experiment; 4.3.1 Experimental Platform; 4.3.2 Image-Based Experiments; 4.4 Experiment Results; 4.5 Conclusion; References; 5 LRF-Based Road Terrain Classification; 5.1 Geometric Arrangement of the LRF; 5.2 Reconstruction of the Road Surface; 5.2.1 Range Data Processing; 5.2.2 Speed Data Processing; 5.2.3 Road Surface; 5.3 Feature Matrix; 5.4 Experiment; 5.4.1 Experimental Platform; 5.4.2 LRF-Based Experiments; 5.5 Experiment Results; 5.5.1 Speed Independency; 5.5.2 LRF-Based Experiment
5.6 ConclusionReferences; 6 Multiple-Sensor Based Road Terrain Classification; 6.1 Predicting LRF-Based Probe; 6.2 Markov Random Field; 6.2.1 Conditional Independence Properties; 6.2.2 Factorization Properties; 6.3 Establishment of MRF Application; 6.3.1 Nodes in MRF; 6.3.2 Variable Values of Nodes in MRF; 6.3.3 Clique Potentials in MRF; 6.3.4 Values of Clique Potentials in MRF; 6.3.5 Energy Function; 6.3.6 Optimization; 6.4 Experiment; 6.4.1 Experimental Platform; 6.4.2 Multiple-Sensor Fusion Based Experiment; 6.5 Experiment Results; 6.6 Conclusion; References; 7 Summary; 7.1 Conclusion